Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Software Defect Prediction

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    HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Software Defect Prediction Framework
    (IEEE Computer Society, 2025) Das, M.; Mohan, B.R.; Guddeti, R.M.R.
    Defect prediction plays a crucial role for any software system across various domains, as its failure may cause unavoidable and undeniable scenarios. For reliable software, defect-free is considered as one of the most important criteria. This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper introduces a hybrid swarm optimization algorithm (SOA) referred to as the gravitational force Lévy flight grasshopper optimization algorithm-artificial bee colony (GFLFGOA-ABC) algorithm. By combining the enhanced exploration feature of the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA) with the robust exploitation capacity of the artificial bee colony (ABC), the GFLFGOA-ABC algorithm is proposed. Prior to validating the HSoMLSDP framework, the LFGFGOA-ABC algorithm's performance is first confirmed by experiments on 6 benchmark functions (BFs) to assess its mean and convergence rate. Following BF verification, the second experiment tunes the hyperparameters of ML models (ANN, GB, XGB) to improve the defect accuracy of the SoMLDP model. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework. © 2025 IEEE.
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    Improving Machine Learning Models with Hybrid Metaheuristic Algorithm for Software Defect Prediction
    (Springer Science and Business Media Deutschland GmbH, 2025) Das, M.; Prasad, N.; Mohan, B.R.
    Software defect prediction has always been an area of interest in the field of software engineering. As the prediction of software defects plays a vital role, researchers are focusing more on metaheuristic algorithms to develop better prediction models. In this paper, we focused on the parameter tuning of the machine learning (ML) models using hybrid metaheuristic algorithms. Here, we have used three metaheuristic algorithms, namely sparrow search, wolf pack, and artificial bee colony optimization algorithm (ABC), to optimize the hyperparameters of the ML model. We have developed a hybrid version of these algorithms for better performance. The sparrow search algorithm (SSA) has high search accuracy and slow convergence speed with the advantages of good stability and strong robustness. The Wolf Pack Algorithm (WPA) has a robust global optimization ability, fast convergence speed, and various optimization strategies. The artificial bee colony (ABC) optimization algorithm has the advantage of not being influenced by the initial parameters, thus enabling search in a wider search space. Considering the strongest features of the aforementioned algorithms, two new hybrid algorithms have been developed, namely sparrow search algorithm-wolf pack algorithm (SSA-WPA) and sparrow search algorithm-artificial bee colony (SSA-ABC). These two algorithms are combined with the artificial neural network and XGBOOST model for better accuracy. To achieve the correctness of the proposed method, it is being verified by five defective NASA datasets and compared with the base methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.